library(ggplot2)
library(plotly)
library(htmlwidgets)
library(egg)
library(gridExtra)
library(grid)
Getting data
df_link <- "https://github.com/vetszabolcs/suicide_data/raw/main/master.csv"
df <- read.csv(df_link)
colnames(df)[1] <- "country"
Preprocessing
df$per_country <- NA
for (y in unique(df$country)){
rows <- which(df$country == y)
df[rows, "per_country"] <- sum(df[rows,"suicides_no"]) / sum(df[rows,"population"]) * 100000
}
developing <- c("Algeria","Egypt","Libya","Morocco","Sudan","Tunisia","Western Sahara","British Indian Ocean Territory","Burundi","Comoros","Djibouti","Eritrea","Ethiopia","French Southern Territories","Kenya","Madagascar","Malawi","Mauritius","Mayotte","Mozambique","Réunion","Rwanda","Seychelles","Somalia","South Sudan","Uganda","United Republic of Tanzania","Zambia","Zimbabwe","Angola","Cameroon","Central African Republic","Chad","Congo","Democratic Republic of the Congo","Equatorial Guinea","Gabon","Sao Tome and Principe","Botswana","Eswatini","Lesotho","Namibia","South Africa","Benin","Burkina Faso","Cabo Verde","Côte d’Ivoire","Gambia","Ghana","Guinea","Guinea-Bissau","Liberia","Mali","Mauritania","Niger","Nigeria","Saint Helena","Senegal","Sierra Leone","Togo","Anguilla","Antigua and Barbuda","Aruba","Bahamas","Barbados","Bonaire","British Virgin Islands","Cayman Islands","Cuba","Curaçao","Dominica","Dominican Republic","Grenada","Guadeloupe","Haiti","Jamaica","Martinique","Montserrat","Puerto Rico","Saint Barthélemy","Saint Kitts and Nevis","Saint Lucia","Saint Martin (French Part)","Saint Vincent and the Grenadines","Sint Maarten (Dutch part)","Trinidad and Tobago","Turks and Caicos Islands","United States Virgin Islands","Belize","Costa Rica","El Salvador","Guatemala","Honduras","Mexico","Nicaragua","Panama","Argentina","Bolivia (Plurinational State of)","Bouvet Island","Brazil","Chile","Colombia","Ecuador","Falkland Islands (Malvinas)","French Guiana","Guyana","Paraguay","Peru","South Georgia and the South Sandwich Islands","Suriname","Uruguay","Venezuela (Bolivarian Republic of)","Kazakhstan","Kyrgyzstan","Tajikistan","Turkmenistan","Uzbekistan","China","China","China","Democratic People's Republic of Korea","Mongolia","Republic of Korea","Brunei Darussalam","Cambodia","Indonesia","Lao People's Democratic Republic","Malaysia","Myanmar","Philippines","Singapore","Thailand","Timor-Leste","Viet Nam","Afghanistan","Bangladesh","Bhutan","India","Iran (Islamic Republic of)","Maldives","Nepal","Pakistan","Sri Lanka","Armenia","Azerbaijan","Bahrain","Georgia","Iraq","Jordan","Kuwait","Lebanon","Oman","Qatar","Saudi Arabia","State of Palestine","Syrian Arab Republic","Turkey","United Arab Emirates","Yemen","Fiji","New Caledonia","Papua New Guinea","Solomon Islands","Vanuatu","Guam","Kiribati","Marshall Islands","Micronesia (Federated States of)","Nauru","Northern Mariana Islands","Palau","United States Minor Outlying Islands","American Samoa","Cook Islands","French Polynesia","Niue","Pitcairn","Samoa","Tokelau","Tonga","Tuvalu","Wallis and Futuna Islands")
developing <- unique(gsub("-|\\(.*", "", developing))
developed <- c("Bermuda","Canada","Greenland","Saint Pierre and Miquelon","United States of America","Japan","Cyprus","Israel","Belarus","Bulgaria","Czech Republic","Hungary","Poland","Republic of Moldova","Romania","Russian Federation","Slovakia","Ukraine","Åland Islands","Guernsey","Jersey","Sark","Denmark","Estonia","Faroe Islands","Finland","Iceland","Ireland","Isle of Man","Latvia","Lithuania","Norway","Svalbard and Jan Mayen Islands","Sweden","United Kingdom of Great Britain and Northern Ireland","Albania","Andorra","Bosnia and Herzegovina","Croatia","Gibraltar","Greece","Holy See","Italy","Malta","Montenegro","North Macedonia","Portugal","San Marino","Serbia","Slovenia","Spain","Austria","Belgium","France","Germany","Liechtenstein","Luxembourg","Monaco","Netherlands","Switzerland","Australia","Christmas Island","Cocos (Keeling) Islands","Heard Island and McDonald Islands","New Zealand","Norfolk Island")
developed <- unique(gsub("-|\\(.*", "", developed))
developed[grep("United", developed, ignore.case = T)][1] <- "United States"
developed[grep("United", developed, ignore.case = T)][2] <- "United Kingdom"
post_soc <- c("Czech Republic", "Estonia", "Lithuania", "Poland",
"Slovakia", "Hungary", "Croatia", "Latvia", "Romania", "Russian Federation",
"Bulgaria", "Serbia", "Ukraine")
west <- c("Austria","Belgium","France","Germany","Liechtenstein","Luxembourg","Monaco",
"Netherlands","Switzerland")
east <- c("Belarus","Bulgaria","Czech Republic","Hungary","Poland","Republic of Moldova","Romania","Russian Federation","Slovakia",
"Ukraine")
north <- c("Denmark","Estonia","Faroe Islands","Finland","Iceland","Ireland","Isle of Man","Latvia","Lithuania","Norway","Svalbard and Jan Mayen Islands","Sweden","United Kingdom")
south <- c("Albania","Andorra","Bosnia and Herzegovina","Croatia","Gibraltar","Greece","Holy See","Italy","Malta","Montenegro","North Macedonia","Portugal","San Marino","Serbia","Slovenia","Spain")
df$developed <- NA
df$developed[df$country %in% developed] <- 1
df$developed[df$country %in% developing] <- 0
developed[grep("Czech", developed)] <- "Czech Republic"
developing[grep("Vinc", developing)] <- "Saint Vincent and Grenadines"
df <- df[!df$country=="Macau",]
df$developed[df$country %in% developed] <- "Developed"
df$developed[df$country %in% developing] <- "Developing"
Slicing european countries
eur <- df[df$country %in% west | df$country %in% east | df$country %in% south |
df$country %in% north | df$country %in% post_soc,]
eur$post_soc <- "Non-postsocialist"
eur$post_soc[eur$country %in% post_soc] <- "Postsocialist"
unique(eur$country[!eur$post_soc == 1])
## [1] "Albania" "Austria" "Belarus" "Belgium" "Bosnia and Herzegovina" "Bulgaria" "Croatia" "Czech Republic" "Denmark" "Estonia" "Finland" "France" "Germany" "Greece" "Hungary" "Iceland" "Ireland" "Italy" "Latvia" "Lithuania" "Luxembourg" "Malta" "Montenegro" "Netherlands" "Norway" "Poland" "Portugal" "Romania" "Russian Federation" "San Marino" "Serbia" "Slovakia" "Slovenia" "Spain" "Sweden" "Switzerland" "Ukraine" "United Kingdom"
eur$post_soc <- as.character(eur$post_soc)
eur$region <- NA
eur$region[eur$country %in% east] <- "east"
eur$region[eur$country %in% west] <- "west"
eur$region[eur$country %in% north] <- "north"
eur$region[eur$country %in% south] <- "south"
Function that creates weighted suicide rates
aggreGate <- function(data_f, by="country", by2="year", new_col){
new_df <- data_f
for (x in unique(new_df[,by])){
for (y in unique(new_df[,by2])){
rows <- which(new_df[,by]== x & new_df[,by2]== y)
new_df[rows, new_col] <- sum(new_df[rows,"suicides_no"]) / sum(new_df[rows,"population"]) * 100000
}}
return(new_df)}
Average yearly suicide
df$yearly <- NA
for (y in unique(df$year)){
rows <- which(df$year == y)
df[rows, "yearly"] <- sum(df[rows,"suicides_no"]) / sum(df[rows,"population"]) * 100000
}
gg0 <- ggplot(df, aes(year, yearly, colour = yearly*-1))+
geom_line(size=1.5)+
theme(legend.position = "none")+
scale_x_continuous(breaks = seq(1985, 2016, 6))+
labs(title = "Yearly suicide", x = "Year", y = "Suicide / 100k people")+
theme(axis.title.x = element_text(hjust = 0.515,
margin=margin(5,0,0,0)))+
theme(axis.title = element_text(face = "bold", size = 12))+
theme(plot.title = element_text(face = "bold", size = 14, hjust = 0.5))+
theme(axis.text = element_text(size=10))
gg0
Theme variable to maintain plot uniformity
my_theme <- theme(axis.title.x = element_text(hjust = 0.52,
margin=margin(5,0,0,0)))+
theme(axis.title = element_text(face = "bold", size = 14))+
theme(plot.title = element_text(face = "bold", size = 16, hjust = 0.5))+
theme(axis.text = element_text(size=10))
Yearly suicide of developed vs. developing countries
df <- aggreGate(df, "year", "developed", "dev_yearly")
gg1 <- ggplot(df, aes(year, dev_yearly, group = developed, colour = developed))+
geom_line(size=1.5)+
labs(title = "Yearly suicide in developed vs. developing countires",
x = "Year", y = "Suicide / 100k people")+
scale_x_continuous(breaks = seq(1985, 2016, 6))+
theme(legend.title = element_blank())+
my_theme+
theme(legend.text = element_text(size = 10))
gg1
Boxplot version of the previous chart
gg2 <- ggplot(df, aes(developed, dev_yearly))+
geom_point(alpha = 0.05, position=position_jitter(height=.05, width=.38),
colour = "red")+
geom_boxplot(aes(colour = dev_yearly), alpha = 0.5, outlier.colour = NA)+
labs(title = "Yearly suicide in developed vs. developing countires",
x = "Country type", y = "Suicide / 100k people")+
my_theme
gg2
Suicide rates in “top” 10 countries
df$per_country <- NA
for (y in unique(df$country)){
rows <- which(df$country == y)
df[rows, "per_country"] <- sum(df[rows,"suicides_no"]) / sum(df[rows,"population"]) * 100000
}
df <- aggreGate(df, "country", "year", "country_yearly")
df <- df[order(c(df$per_country), decreasing = T),]
df$country <- factor(x =df$country, levels = unique(df$country))
top10 <- ggplot(df[df$country %in% head(unique(df$country), 10),],
aes(x=year, y=country_yearly,
colour=country, group=country,
text = paste("Country:", country, "\n",
"Year: ", year, "\n",
"Suicide/100k: ", round(country_yearly), sep="")))+
geom_line(size=1.1)+
labs(title = "Yearly suicide in top 10 countries",
x = "Year", y = "Suicide / 100k people",
color = "Country")+
theme(legend.title = element_text(face = "bold", size = 16))+
theme(legend.text = element_text(size = 14))+
theme(axis.title.x = element_text(hjust = 0.515))+
scale_x_continuous(breaks = seq(1985, 2016, 6))+
my_theme+
geom_point(aes(color=country), cex = 1.5)
ggplotly(top10+theme(legend.title = element_blank()), tooltip = "text")
Suicide rates in “top” 10 european countries
eur <- aggreGate(eur, "country", "year", "country_yearly")
eur <- eur[order(eur$per_country, decreasing = T),]
head(unique(eur$country), 10)
## [1] "Lithuania" "Russian Federation" "Belarus" "Hungary" "Latvia" "Slovenia" "Estonia" "Ukraine" "Finland" "Belgium"
eur$country <- factor(eur$country, levels = unique(eur$country))
top10_eur <- ggplot(eur[eur$country %in% head(unique(eur$country), 10),],
aes(x=year, y=country_yearly,
colour=country, group=country,
text = paste("Country:", country, "\n",
"Year: ", year, "\n",
"Suicide/100k: ", round(country_yearly), sep="")))+
geom_line(size=1.1)+
labs(title = "Yearly suicide in top 10 european countries",
x = "Year", y = "Suicide / 100k people",
color = "Country")+
theme(legend.title = element_text(face = "bold", size = 16))+
theme(legend.text = element_text(size = 12))+
theme(axis.title.x = element_text(hjust = 0.515))+
scale_x_continuous(breaks = seq(1985, 2016, 6))+
my_theme+
geom_point(aes(color=country), cex = 1.5)
ggplotly(top10_eur+theme(legend.title = element_blank()), tooltip = "text")
Postsocialist vs. non-postsocialist countries
eur <- aggreGate(eur, "country", "year", "per_country_year")
options(scipen=10000)
gg4 <- ggplot(eur,aes(gdp_per_capita...., per_country_year, colour = post_soc))+
geom_point(size=3)+
labs(title = "Suicide and GDP in post -and non-postsocialist\neuropean countries",
x = "Yearly GDP per capital", y = "Yearly Suicide / 100k people")+
theme(legend.title = element_blank())+
theme(legend.text = element_text(size = 12))+
theme(axis.title.x = element_text(hjust = 0.515))+
my_theme+
theme(axis.title.x = element_text(margin=margin(10,0,0,0)))+
theme(axis.title.y = element_text(margin=margin(0,10,0,0)))
gg4
Regions and age in EU
eur <- aggreGate(eur, "region","age","per_region_age")
eur$age <- factor(eur$age, levels = c("5-14 years", "15-24 years", "25-34 years",
"35-54 years","55-74 years", "75+ years"))
gg5 <- ggplot(eur,aes(region, per_region_age, fill = age))+
geom_bar(stat = "identity", position = "dodge")+
ggtitle("European regions and age")+
xlab("Region")+ylab("Suicide / 100k people")+
labs(fill="Age")+
theme(text = element_text(size = 14))+
theme(legend.title = element_text(face = "bold", size=12))+
my_theme+
scale_fill_hue(l = 50, c = 50)
gg5
Region and sex in EU
eur <- aggreGate(eur, "region", "sex", "per_region_sex")
gg6 <- ggplot(eur,aes(region, per_region_sex, fill = sex))+
geom_bar(stat = "identity", position = "dodge")+
ggtitle("European regions and age")+
xlab("Region")+ylab("Suicide / 100k people")+
labs(fill="Sex")+
theme(text = element_text(size = 14))+
theme(legend.title = element_text(face = "bold", size=12))+
my_theme+
scale_fill_hue(l = 50, c = 50)
gg6
Map of Europe
df$c_code <- countrycode::countryname(df$country, "iso3c")
df <- aggreGate(df, new_col = "year_country")
df$year_country <- round(df$year_country, 1)
df$hover <- paste(df$country, paste(df$year_country, " /100k", sep = ""), sep = "\n")
label <- list(bgcolor = "black",
bordercolor = "transparent",
font = list(size = 15, color = "white"))
plot_geo(df[df$year>=1989 & df$year<2016,], locationmode = "countries",
frame = ~year,
width = 800, height = 800) %>%
add_trace(locations = ~c_code,
z = ~year_country,
zmin = mean(df$year_country[df$year>=1989 & df$year<2016])/3,
zmax = mean(df$year_country[df$year>=1989 & df$year<2016])*3,
color = ~year_country,
colorscale = "RdBu",
text = ~hover,
hoverinfo = "text") %>%
layout(geo = list(scope = "europe"),
title = list(text = "<b>Yearly suicides in european countries</b>",
y = 0.95, font = list(size = 20)),
margin = list(t = 200)) %>%
style(hoverlabel = label) %>%
colorbar(title = "<b>Suicide</b>", y = 0.85, ticksuffix = "/100k")
Combining them together
facet <- arrangeGrob(gg0+theme(axis.title.x = element_blank()),
gg5+theme(legend.position = "right",
legend.title = element_blank()),
top10+theme(axis.title.x = element_blank())+
theme(legend.position = "bottom"),
gg4+theme(legend.position = "right"),
gg1+theme(axis.title.x = element_blank())+
theme(legend.position = "bottom"),
gg6+theme(legend.position = "right",
legend.title = element_blank()), nrow = 3, ncol=2,
top = textGrob("Suicide rates from 1987 to 2016\n",
gp=gpar(fontsize=14, fontface="bold"))) %>%
ggplotify::as.ggplot() %>%
ggsave("facet.png",plot = ., device = "png",
width = 25, height = 15, dpi = 150)
knitr::include_graphics("facet.png")
